Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors
Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.
Cancer exerts a great impact on the quality of life and is a leading cause of death worldwide. Although cancer chemotherapy, one of the major medical advances in the last few decades, is directed toward certain macromolecules to treat cancer, it cannot efficiently discriminate between normally dividing cell and tumor cells, leading to unwanted toxic side effects. However, targets are usually located in tumor cells, thus providing a high specificity toward tumor cells and broader therapeutic window with less toxicity is beneficial. Therefore, targeted therapy represents a promising approach to cancer therapy. Generally an ideal therapeutic target should not only be susceptible to specific inhibition by small ligands but tumor cells also more dependent on the activity of the target than normal cells.
Janus kinase 2 (JAK2) is a member of the Janus family of tyrosine kinase, which plays an important role in many cellular signaling pathways. It is a nonreceptor tyrosine kinase that relays signals from cytokine receptors to downstream targets, including the transcription factors STAT3 and STAT5. When it is activated, this family of enzymes increase tumor cell proliferation and growth, induce antiapoptotic effects and promote angiogenesis as well as metastasis. Therefore, the inhibition of JAK2 would greatly reduce the activity of tyrosine kinase and compounds achieving such effects are known as JAK2 inhibitors.
JAK inhibitors are important class of targeted therapy that interfere with specific cell signaling pathways, which allows target-specific therapy for selected malignancies. Many of the JAK inhibitors are known to interfere with the JAK-STAT pathways, which have an implication in the treatment of different types of cancers and inflammatory diseases. JAK inhibitors can be found in FDA approved drugs and clinical trials. For example, Ruxolitinib, an orally bioavailable selective inhibitor of JAK2, inhibits the proliferation of JAK2. Lestautinib, an orally bio-available polyaromatic indolocarbozole alkaloid, is a tyrosine kinase inhibitor that is currently in clinical trials and assigned Investigational New Drug (IND) number 76431.
Quantitative structure activity relationship (QSAR) is an approach for elucidating the origin of biological activity with their respective chemical compounds represented as descriptors. The QSAR models can reveal molecular features that are essential for active compounds and that can subsequently be used as therapeutic agents. Several QSAR models were developed in the hope to drive the novel compounds with better properties against kinase. To understand the origin and bioactivities of JAK inhibitors, models were developed with the hope to identify important pharmacophores and substructures using pharmacophores and 3D QSAR. Due to the polypharmacological nature of compounds, multi-target QSAR models have been also developed to handle the interaction of multiple targets of JAK inhibitors. Although pharmacophores and 3D QSAR models, as well as multi-target QSAR models and tools are essential in understanding structure-activity relationship of JAK2 inhibitors, the ability of the those models to predict unknown bioactivity properties depends largely on the size of training sets. Extrapolation power of the model, where the model predicts accurately with confidence and credibility, depends on how well the training data represent the unknown compounds.
Therefore, QSAR model will have a small applicability domain and low general predictability if they are based on a small data set. Here they propose a large-scale QSAR investigation for predicting JAK2 inhibitors. Several statistical methods were used to build regression models in which inhibitors were represented as highly interpretable substructure fingerprint descriptors to understand the underlying JAK2 inhibitory activity, which is performed according to the guidelines of Organisation for Economic Cooperation and Development (OECD). This may provide important insights into the structural basis for the inhibition of JAK2, which may aid in the fight against cancer, in particular myeloproliferative neoplasms.1
Simeon, S., & Jongkon, N. (2019). Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors. Molecules (Basel, Switzerland), 24(23), 4393. https://doi.org/10.3390/molecules24234393